Recommendation systems

Loading the data directly into radiant

if (file.exists("data/cf_demo.rds")) {
  r_data[["cf_demo_wrk"]] <- readr::read_rds("data/cf_demo.rds")
  register("cf_demo_wrk")
  
  r_data[["ratings0_wrk"]] <- readr::read_rds("data/ratings0.rds")
  register("ratings0_wrk")
  
  r_data[["ratings50_wrk"]] <- readr::read_rds("data/ratings50.rds")
  register("ratings50_wrk")
  
  r_data[["ratings80_wrk"]] <- readr::read_rds("data/ratings80.rds")
  register("ratings80_wrk")
  
  r_data[["ulratings_wrk"]] <- readr::read_rds("data/ulratings.rds")
  register("ulratings_wrk")
} else {
  stop("Are you using the Rstudio project folder for 'crs'?\\nIt should say 'Project: crs' at the top-right of your screen",  call. = FALSE)
}

Demo: Colaborative Filtering

We will start with the cf_demo_wrk data. See the dataset description in Data > Manage. The results are equivalent to what you will see in the cf_demo.xlsx file. Note the data filter used to estimate the model on a training dataset (i.e., U1-U10) and predict for user U11

result <- crs(
  dataset = "cf_demo_wrk", 
  id = "users", 
  prod = "movies", 
  pred = c("M6", "M7", "M8", "M9", "M10"), 
  rate = "ratings", 
  data_filter = "training == 1"
)
summary(result)
Collaborative filtering
Data       : cf_demo_wrk
Filter     : training == 1
User id    : users
Product id : movies
Predict for: M6, M7, M8, M9, M10 

Recommendations:

 users product rating average   cf ranking avg_rank cf_rank
   U11      M6           3.30 4.10                3       1
   U11      M7           2.70 2.08                5       4
   U11      M8           3.50 1.70                2       5
   U11      M9           2.90 2.13                4       3
   U11     M10           4.10 2.71                1       2

Frequency plot of ratings in the validation data

For user in the hold-out sample we have ratings on movies 14-25

result <- pivotr(
  dataset = "ratings0_wrk", 
  cvars = "rating", 
  data_filter = "training == 0 & product %in% paste0('mov', 14:25)", 
  nr = 5
)
plot(result, custom = TRUE) +
  labs(title = "Ratings for users 61-100 on movies 14-25")

Generating recommendations with CF

result <- crs(
  dataset = "ratings0_wrk", 
  id = "user", 
  prod = "product", 
  pred = "mov14:mov25", 
  rate = "rating", 
  data_filter = "training == 1"
)
summary(result)
Collaborative filtering
Data       : ratings0_wrk
Filter     : training == 1
User id    : user
Product id : product
Predict for: mov14, mov15, mov16, mov17, mov18, mov19, mov20, mov21, mov22, mov23, mov24, mov25 
Rows shown : 36 out of 480 

Summary:

- Average rating picks the best product 30.0% of the time
- Collaborative filtering picks the best product 60.0% of the time
- Pick based on average rating is in the top 3 products 47.5% of the time
- Pick based on collaborative filtering is in the top 3 products 80.0% of the time
- Top 3 based on average ratings contains the best product 60.0% of the time
- Top 3 based on collaborative filtering contains the best product 80.0% of the time

Recommendations:

 user product rating average   cf ranking avg_rank cf_rank
   61   mov14      4    3.55 1.89       1        1       4
   61   mov15      2    2.78 2.38       6       10       3
   61   mov16      3    3.02 1.71       2        6       6
   61   mov17      3    2.60 1.78       2       12       5
   61   mov18      2    3.15 2.50       6        4       1
   61   mov19      1    2.67 1.14      11       11      12
   61   mov20      3    3.05 1.47       2        5      10
   61   mov21      2    2.92 1.50       6        9       9
   61   mov22      2    3.20 1.61       6        3       8
   61   mov23      1    3.38 1.66      11        2       7
   61   mov24      2    2.97 2.39       6        7       2
   61   mov25      3    2.93 1.46       2        8      11
   62   mov14      2    3.55 2.60       6        1       9
   62   mov15      4    2.78 2.67       2       10       8
   62   mov16      5    3.02 4.63       1        6       1
   62   mov17      4    2.60 4.16       2       12       4
   62   mov18      1    3.15 1.87      10        4      12
   62   mov19      1    2.67 3.25      10       11       7
   62   mov20      3    3.05 4.44       4        5       2
   62   mov21      3    2.92 4.25       4        9       3
   62   mov22      2    3.20 3.27       6        3       6
   62   mov23      1    3.38 2.09      10        2      11
   62   mov24      2    2.97 2.28       6        7      10
   62   mov25      2    2.93 3.57       6        8       5
   63   mov14      2    3.55 2.59       7        1      10
   63   mov15      2    2.78 3.01       7       10       7
   63   mov16      4    3.02 4.33       3        6       1
   63   mov17      5    2.60 3.98       1       12       3
   63   mov18      2    3.15 2.25       7        4      11
   63   mov19      1    2.67 2.77      11       11       8
   63   mov20      5    3.05 4.00       1        5       2
   63   mov21      3    2.92 3.88       4        9       4
   63   mov22      3    3.20 3.05       4        3       6
   63   mov23      1    3.38 1.96      11        2      12
   63   mov24      2    2.97 2.64       7        7       9
   63   mov25      3    2.93 3.25       4        8       5
plot(result)

result <- crs(
  dataset = "ratings50_wrk", 
  id = "user", 
  prod = "product", 
  pred = "mov14:mov25", 
  rate = "rating", 
  data_filter = "training == 1"
)
plot(result)

result <- crs(
  dataset = "ratings80_wrk", 
  id = "user", 
  prod = "product", 
  pred = "mov14:mov25", 
  rate = "rating", 
  data_filter = "training == 1"
)
plot(result)

Predictions using regression across all users

result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("talk", "sex", "action", "story"), 
  data_filter = "training == 1"
)
summary(result)
Linear regression (OLS)
Data     : ulratings_wrk 
Filter   : training == 1 
Response variable    : rating 
Explanatory variables: talk, sex, action, story 
Null hyp.: the effect of x on rating is zero
Alt. hyp.: the effect of x on rating is not zero

             coefficient std.error t.value p.value    
 (Intercept)       3.362     0.151  22.223  < .001 ***
 talk             -0.032     0.014  -2.308   0.021 *  
 sex              -0.050     0.020  -2.534   0.011 *  
 action            0.100     0.024   4.105  < .001 ***
 story            -0.091     0.024  -3.738  < .001 ***

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

R-squared: 0.048,  Adjusted R-squared: 0.046 
F-statistic: 16.485 df(4,1295), p.value < .001
Nr obs: 1,300 
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
Linear regression (OLS)
Data                 : ulratings_wrk 
Filter               : training == 1 
Response variable    : rating 
Explanatory variables: talk, sex, action, story 
Prediction dataset   : ulratings_wrk 
Rows shown           : 10 of 2,500 

 talk sex action story Prediction   2.5% 97.5%   +/-
    1   7      9     9      3.063  0.361 5.764 2.701
    9   5      6     6      2.882  0.181 5.583 2.701
    1   4     10     7      3.492  0.791 6.194 2.701
   10   6      1     2      2.666 -0.038 5.369 2.704
    3   7      5     5      2.964  0.263 5.665 2.701
    7   9      8     7      2.855  0.155 5.556 2.700
    9   8      8     4      3.114  0.407 5.820 2.706
    2  10     10     9      2.981  0.279 5.684 2.702
    6   1      7     4      3.457  0.753 6.161 2.704
    5   7      9    10      2.845  0.144 5.547 2.702
store(pred, data = "ulratings_wrk", name = "aggregate")

User-level predictions using regression

Predictions based only on ratings from user 1 on movies 1-13 (note the filter).

result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("talk", "sex", "action", "story"), 
  data_filter = "user == 1 & training == 1"
)
summary(result)
Linear regression (OLS)
Data     : ulratings_wrk 
Filter   : user == 1 & training == 1 
Response variable    : rating 
Explanatory variables: talk, sex, action, story 
Null hyp.: the effect of x on rating is zero
Alt. hyp.: the effect of x on rating is not zero

             coefficient std.error t.value p.value    
 (Intercept)       2.246     0.371   6.055  < .001 ***
 talk              0.412     0.034  12.243  < .001 ***
 sex              -0.158     0.048  -3.281   0.011 *  
 action           -0.104     0.059  -1.745   0.119    
 story             0.028     0.059   0.469   0.651    

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

R-squared: 0.969,  Adjusted R-squared: 0.954 
F-statistic: 63.344 df(4,8), p.value < .001
Nr obs: 13 
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
Linear regression (OLS)
Data                 : ulratings_wrk 
Filter               : user == 1 & training == 1 
Response variable    : rating 
Explanatory variables: talk, sex, action, story 
Prediction dataset   : ulratings_wrk 
Rows shown           : 10 of 2,500 

 talk sex action story Prediction   2.5% 97.5%   +/-
    1   7      9     9      0.871 -0.016 1.758 0.887
    9   5      6     6      4.706  3.827 5.585 0.879
    1   4     10     7      1.184  0.291 2.078 0.894
   10   6      1     2      5.367  4.421 6.313 0.946
    3   7      5     5      1.997  1.121 2.874 0.876
    7   9      8     7      3.073  2.204 3.941 0.869
    9   8      8     4      3.970  2.960 4.979 1.010
    2  10     10     9      0.706 -0.213 1.625 0.919
    6   1      7     4      3.943  2.987 4.898 0.956
    5   7      9    10      2.545  1.647 3.443 0.898
store(pred, data = "ulratings_wrk", name = "user1")

Generate predictions for each user separately (note the filter). We could use a loop to do this but we can actually get predictions for all users using interaction terms. Hint: You might need something like this for Pentathlon III

result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("user", "talk", "sex", "action", "story"), 
  int = c("user:talk", "user:sex", "user:action", "user:story"), 
  data_filter = "training == 1"
)
summary(result)
Linear regression (OLS)
Data     : ulratings_wrk 
Filter   : training == 1 
Response variable    : rating 
Explanatory variables: user, talk, sex, action, story 
Null hyp.: the effect of x on rating is zero
Alt. hyp.: the effect of x on rating is not zero

                 coefficient std.error t.value p.value    
 (Intercept)           2.246     0.694   3.235   0.001 ** 
 user|2                0.889     0.982   0.906   0.365    
 user|3                1.056     0.982   1.075   0.283    
 user|4                0.232     0.982   0.236   0.813    
 user|5                2.105     0.982   2.144   0.032 *  
 user|6               -0.319     0.982  -0.325   0.746    
 user|7                0.654     0.982   0.666   0.505    
 user|8                0.687     0.982   0.700   0.484    
 user|9                1.241     0.982   1.264   0.207    
 user|10               0.650     0.982   0.662   0.508    
 user|11               1.270     0.982   1.293   0.196    
 user|12               1.849     0.982   1.883   0.060 .  
 user|13               2.072     0.982   2.110   0.035 *  
 user|14               1.280     0.982   1.304   0.193    
 user|15               0.782     0.982   0.797   0.426    
 user|16               0.596     0.982   0.607   0.544    
 user|17               0.998     0.982   1.017   0.310    
 user|18               1.148     0.982   1.170   0.243    
 user|19               1.817     0.982   1.851   0.065 .  
 user|20               1.225     0.982   1.247   0.213    
 user|21              -0.526     0.982  -0.536   0.592    
 user|22               0.984     0.982   1.002   0.317    
 user|23               0.241     0.982   0.246   0.806    
 user|24               0.780     0.982   0.795   0.427    
 user|25              -0.074     0.982  -0.075   0.940    
 user|26               1.278     0.982   1.302   0.193    
 user|27               1.041     0.982   1.061   0.289    
 user|28               0.997     0.982   1.016   0.310    
 user|29               2.499     0.982   2.545   0.011 *  
 user|30              -0.241     0.982  -0.246   0.806    
 user|31               1.052     0.982   1.072   0.284    
 user|32               1.708     0.982   1.740   0.082 .  
 user|33               1.058     0.982   1.078   0.281    
 user|34               1.450     0.982   1.477   0.140    
 user|35               0.734     0.982   0.748   0.455    
 user|36              -0.158     0.982  -0.161   0.872    
 user|37               0.815     0.982   0.831   0.406    
 user|38               2.005     0.982   2.042   0.041 *  
 user|39               2.807     0.982   2.859   0.004 ** 
 user|40               1.558     0.982   1.587   0.113    
 user|41               0.840     0.982   0.855   0.393    
 user|42               1.221     0.982   1.243   0.214    
 user|43               1.566     0.982   1.595   0.111    
 user|44               2.225     0.982   2.266   0.024 *  
 user|45               1.015     0.982   1.034   0.302    
 user|46               1.658     0.982   1.689   0.092 .  
 user|47               0.980     0.982   0.998   0.319    
 user|48               0.634     0.982   0.646   0.518    
 user|49               0.490     0.982   0.499   0.618    
 user|50               1.580     0.982   1.610   0.108    
 user|51               0.813     0.982   0.828   0.408    
 user|52               2.734     0.982   2.785   0.005 ** 
 user|53               1.304     0.982   1.329   0.184    
 user|54               1.141     0.982   1.162   0.246    
 user|55               1.986     0.982   2.023   0.043 *  
 user|56               1.507     0.982   1.535   0.125    
 user|57               0.567     0.982   0.577   0.564    
 user|58               1.780     0.982   1.813   0.070 .  
 user|59               0.444     0.982   0.452   0.651    
 user|60               1.581     0.982   1.611   0.108    
 user|61               0.044     0.982   0.045   0.964    
 user|62               1.606     0.982   1.636   0.102    
 user|63               1.316     0.982   1.340   0.181    
 user|64               1.932     0.982   1.968   0.049 *  
 user|65              -0.276     0.982  -0.282   0.778    
 user|66               1.069     0.982   1.088   0.277    
 user|67               1.039     0.982   1.059   0.290    
 user|68               1.526     0.982   1.554   0.120    
 user|69              -0.358     0.982  -0.365   0.715    
 user|70               1.526     0.982   1.554   0.120    
 user|71              -0.697     0.982  -0.710   0.478    
 user|72               1.986     0.982   2.023   0.043 *  
 user|73               0.342     0.982   0.348   0.728    
 user|74               0.050     0.982   0.050   0.960    
 user|75               0.629     0.982   0.640   0.522    
 user|76               1.306     0.982   1.331   0.184    
 user|77               1.042     0.982   1.062   0.289    
 user|78               1.322     0.982   1.346   0.179    
 user|79               1.790     0.982   1.823   0.069 .  
 user|80               1.594     0.982   1.624   0.105    
 user|81               0.381     0.982   0.388   0.698    
 user|82               1.751     0.982   1.784   0.075 .  
 user|83               1.901     0.982   1.936   0.053 .  
 user|84               1.351     0.982   1.377   0.169    
 user|85               1.918     0.982   1.953   0.051 .  
 user|86               0.349     0.982   0.355   0.723    
 user|87               0.387     0.982   0.394   0.694    
 user|88               2.672     0.982   2.722   0.007 ** 
 user|89               2.289     0.982   2.331   0.020 *  
 user|90               1.265     0.982   1.289   0.198    
 user|91               0.900     0.982   0.917   0.359    
 user|92               1.732     0.982   1.765   0.078 .  
 user|93               1.932     0.982   1.968   0.049 *  
 user|94               0.664     0.982   0.676   0.499    
 user|95              -0.457     0.982  -0.466   0.641    
 user|96               1.382     0.982   1.408   0.160    
 user|97               1.059     0.982   1.079   0.281    
 user|98               0.818     0.982   0.833   0.405    
 user|99               1.699     0.982   1.731   0.084 .  
 user|100              2.557     0.982   2.605   0.009 ** 
 talk                  0.412     0.063   6.541  < .001 ***
 sex                  -0.158     0.090  -1.753   0.080 .  
 action               -0.104     0.111  -0.933   0.351    
 story                 0.028     0.111   0.251   0.802    
 user|2:talk          -0.149     0.089  -1.669   0.096 .  
 user|3:talk          -0.035     0.089  -0.392   0.695    
 user|4:talk          -0.061     0.089  -0.681   0.496    
 user|5:talk          -0.222     0.089  -2.496   0.013 *  
 user|6:talk           0.064     0.089   0.724   0.469    
 user|7:talk          -0.054     0.089  -0.606   0.544    
 user|8:talk          -0.182     0.089  -2.048   0.041 *  
 user|9:talk          -0.050     0.089  -0.566   0.572    
 user|10:talk         -0.105     0.089  -1.182   0.238    
 user|11:talk         -0.135     0.089  -1.519   0.129    
 user|12:talk         -0.152     0.089  -1.704   0.089 .  
 user|13:talk         -0.218     0.089  -2.454   0.014 *  
 user|14:talk         -0.198     0.089  -2.220   0.027 *  
 user|15:talk         -0.086     0.089  -0.967   0.334    
 user|16:talk         -0.023     0.089  -0.260   0.795    
 user|17:talk         -0.100     0.089  -1.126   0.260    
 user|18:talk         -0.217     0.089  -2.435   0.015 *  
 user|19:talk         -0.155     0.089  -1.745   0.081 .  
 user|20:talk         -0.285     0.089  -3.209   0.001 ** 
 user|21:talk          0.068     0.089   0.767   0.444    
 user|22:talk         -0.054     0.089  -0.604   0.546    
 user|23:talk         -0.121     0.089  -1.360   0.174    
 user|24:talk         -0.020     0.089  -0.228   0.820    
 user|25:talk          0.017     0.089   0.194   0.846    
 user|26:talk         -0.623     0.089  -7.004  < .001 ***
 user|27:talk         -0.630     0.089  -7.079  < .001 ***
 user|28:talk         -0.526     0.089  -5.910  < .001 ***
 user|29:talk         -0.851     0.089  -9.559  < .001 ***
 user|30:talk         -0.470     0.089  -5.283  < .001 ***
 user|31:talk         -0.647     0.089  -7.270  < .001 ***
 user|32:talk         -0.750     0.089  -8.426  < .001 ***
 user|33:talk         -0.619     0.089  -6.956  < .001 ***
 user|34:talk         -0.679     0.089  -7.626  < .001 ***
 user|35:talk         -0.522     0.089  -5.870  < .001 ***
 user|36:talk         -0.435     0.089  -4.888  < .001 ***
 user|37:talk         -0.634     0.089  -7.125  < .001 ***
 user|38:talk         -0.766     0.089  -8.615  < .001 ***
 user|39:talk         -0.695     0.089  -7.810  < .001 ***
 user|40:talk         -0.766     0.089  -8.605  < .001 ***
 user|41:talk         -0.584     0.089  -6.569  < .001 ***
 user|42:talk         -0.669     0.089  -7.517  < .001 ***
 user|43:talk         -0.673     0.089  -7.562  < .001 ***
 user|44:talk         -0.745     0.089  -8.374  < .001 ***
 user|45:talk         -0.659     0.089  -7.407  < .001 ***
 user|46:talk         -0.682     0.089  -7.669  < .001 ***
 user|47:talk         -0.636     0.089  -7.149  < .001 ***
 user|48:talk         -0.601     0.089  -6.754  < .001 ***
 user|49:talk         -0.593     0.089  -6.663  < .001 ***
 user|50:talk         -0.655     0.089  -7.357  < .001 ***
 user|51:talk         -0.588     0.089  -6.613  < .001 ***
 user|52:talk         -0.615     0.089  -6.916  < .001 ***
 user|53:talk         -0.497     0.089  -5.584  < .001 ***
 user|54:talk         -0.545     0.089  -6.121  < .001 ***
 user|55:talk         -0.660     0.089  -7.413  < .001 ***
 user|56:talk         -0.773     0.089  -8.683  < .001 ***
 user|57:talk         -0.656     0.089  -7.378  < .001 ***
 user|58:talk         -0.570     0.089  -6.404  < .001 ***
 user|59:talk         -0.428     0.089  -4.810  < .001 ***
 user|60:talk         -0.606     0.089  -6.816  < .001 ***
 user|61:talk         -0.567     0.089  -6.373  < .001 ***
 user|62:talk         -0.529     0.089  -5.942  < .001 ***
 user|63:talk         -0.531     0.089  -5.973  < .001 ***
 user|64:talk         -0.721     0.089  -8.104  < .001 ***
 user|65:talk         -0.584     0.089  -6.560  < .001 ***
 user|66:talk         -0.670     0.089  -7.528  < .001 ***
 user|67:talk         -0.655     0.089  -7.357  < .001 ***
 user|68:talk         -0.697     0.089  -7.835  < .001 ***
 user|69:talk         -0.495     0.089  -5.569  < .001 ***
 user|70:talk         -0.625     0.089  -7.024  < .001 ***
 user|71:talk         -0.520     0.089  -5.846  < .001 ***
 user|72:talk         -0.554     0.089  -6.229  < .001 ***
 user|73:talk         -0.537     0.089  -6.034  < .001 ***
 user|74:talk         -0.618     0.089  -6.949  < .001 ***
 user|75:talk         -0.605     0.089  -6.804  < .001 ***
 user|76:talk         -0.559     0.089  -6.288  < .001 ***
 user|77:talk         -0.453     0.089  -5.096  < .001 ***
 user|78:talk         -0.458     0.089  -5.147  < .001 ***
 user|79:talk         -0.445     0.089  -4.999  < .001 ***
 user|80:talk         -0.465     0.089  -5.221  < .001 ***
 user|81:talk         -0.354     0.089  -3.977  < .001 ***
 user|82:talk         -0.437     0.089  -4.915  < .001 ***
 user|83:talk         -0.452     0.089  -5.080  < .001 ***
 user|84:talk         -0.490     0.089  -5.504  < .001 ***
 user|85:talk         -0.486     0.089  -5.467  < .001 ***
 user|86:talk         -0.311     0.089  -3.491  < .001 ***
 user|87:talk         -0.364     0.089  -4.086  < .001 ***
 user|88:talk         -0.426     0.089  -4.784  < .001 ***
 user|89:talk         -0.497     0.089  -5.591  < .001 ***
 user|90:talk         -0.365     0.089  -4.100  < .001 ***
 user|91:talk         -0.408     0.089  -4.583  < .001 ***
 user|92:talk         -0.448     0.089  -5.037  < .001 ***
 user|93:talk         -0.492     0.089  -5.535  < .001 ***
 user|94:talk         -0.422     0.089  -4.739  < .001 ***
 user|95:talk         -0.341     0.089  -3.835  < .001 ***
 user|96:talk         -0.454     0.089  -5.108  < .001 ***
 user|97:talk         -0.406     0.089  -4.560  < .001 ***
 user|98:talk         -0.415     0.089  -4.664  < .001 ***
 user|99:talk         -0.415     0.089  -4.660  < .001 ***
 user|100:talk        -0.529     0.089  -5.946  < .001 ***
 user|2:sex            0.003     0.127   0.027   0.979    
 user|3:sex           -0.050     0.127  -0.397   0.692    
 user|4:sex            0.023     0.127   0.183   0.855    
 user|5:sex           -0.028     0.127  -0.219   0.826    
 user|6:sex            0.018     0.127   0.143   0.886    
 user|7:sex            0.043     0.127   0.336   0.737    
 user|8:sex           -0.066     0.127  -0.517   0.605    
 user|9:sex           -0.049     0.127  -0.382   0.703    
 user|10:sex           0.001     0.127   0.004   0.997    
 user|11:sex          -0.016     0.127  -0.127   0.899    
 user|12:sex          -0.166     0.127  -1.309   0.191    
 user|13:sex           0.060     0.127   0.471   0.638    
 user|14:sex          -0.012     0.127  -0.094   0.925    
 user|15:sex          -0.125     0.127  -0.981   0.327    
 user|16:sex          -0.099     0.127  -0.779   0.436    
 user|17:sex          -0.058     0.127  -0.453   0.651    
 user|18:sex           0.113     0.127   0.890   0.374    
 user|19:sex          -0.044     0.127  -0.343   0.732    
 user|20:sex           0.111     0.127   0.875   0.382    
 user|21:sex           0.017     0.127   0.137   0.891    
 user|22:sex           0.066     0.127   0.519   0.604    
 user|23:sex           0.030     0.127   0.233   0.816    
 user|24:sex          -0.042     0.127  -0.329   0.742    
 user|25:sex           0.039     0.127   0.309   0.758    
 user|26:sex           0.029     0.127   0.231   0.817    
 user|27:sex           0.147     0.127   1.157   0.248    
 user|28:sex           0.063     0.127   0.499   0.618    
 user|29:sex           0.236     0.127   1.854   0.064 .  
 user|30:sex           0.066     0.127   0.521   0.603    
 user|31:sex           0.161     0.127   1.264   0.207    
 user|32:sex           0.108     0.127   0.852   0.394    
 user|33:sex           0.098     0.127   0.774   0.439    
 user|34:sex           0.125     0.127   0.983   0.326    
 user|35:sex           0.073     0.127   0.576   0.565    
 user|36:sex           0.051     0.127   0.404   0.686    
 user|37:sex          -0.024     0.127  -0.188   0.851    
 user|38:sex           0.068     0.127   0.533   0.594    
 user|39:sex          -0.079     0.127  -0.621   0.535    
 user|40:sex           0.121     0.127   0.952   0.341    
 user|41:sex           0.089     0.127   0.700   0.484    
 user|42:sex           0.004     0.127   0.035   0.972    
 user|43:sex          -0.053     0.127  -0.420   0.674    
 user|44:sex          -0.011     0.127  -0.084   0.933    
 user|45:sex           0.092     0.127   0.722   0.470    
 user|46:sex           0.126     0.127   0.989   0.323    
 user|47:sex           0.068     0.127   0.532   0.595    
 user|48:sex           0.031     0.127   0.241   0.810    
 user|49:sex           0.107     0.127   0.841   0.401    
 user|50:sex           0.185     0.127   1.458   0.145    
 user|51:sex           0.478     0.127   3.758  < .001 ***
 user|52:sex           0.436     0.127   3.427  < .001 ***
 user|53:sex           0.346     0.127   2.721   0.007 ** 
 user|54:sex           0.585     0.127   4.602  < .001 ***
 user|55:sex           0.551     0.127   4.330  < .001 ***
 user|56:sex           0.677     0.127   5.325  < .001 ***
 user|57:sex           0.648     0.127   5.096  < .001 ***
 user|58:sex           0.482     0.127   3.789  < .001 ***
 user|59:sex           0.456     0.127   3.583  < .001 ***
 user|60:sex           0.367     0.127   2.890   0.004 ** 
 user|61:sex           0.448     0.127   3.523  < .001 ***
 user|62:sex           0.503     0.127   3.958  < .001 ***
 user|63:sex           0.475     0.127   3.738  < .001 ***
 user|64:sex           0.662     0.127   5.206  < .001 ***
 user|65:sex           0.562     0.127   4.421  < .001 ***
 user|66:sex           0.526     0.127   4.138  < .001 ***
 user|67:sex           0.356     0.127   2.802   0.005 ** 
 user|68:sex           0.597     0.127   4.696  < .001 ***
 user|69:sex           0.428     0.127   3.367  < .001 ***
 user|70:sex           0.398     0.127   3.126   0.002 ** 
 user|71:sex           0.442     0.127   3.473  < .001 ***
 user|72:sex           0.402     0.127   3.161   0.002 ** 
 user|73:sex           0.665     0.127   5.229  < .001 ***
 user|74:sex           0.729     0.127   5.729  < .001 ***
 user|75:sex           0.502     0.127   3.949  < .001 ***
 user|76:sex           0.062     0.127   0.491   0.623    
 user|77:sex           0.020     0.127   0.155   0.877    
 user|78:sex          -0.179     0.127  -1.406   0.160    
 user|79:sex          -0.163     0.127  -1.280   0.201    
 user|80:sex          -0.367     0.127  -2.889   0.004 ** 
 user|81:sex          -0.124     0.127  -0.977   0.329    
 user|82:sex          -0.130     0.127  -1.022   0.307    
 user|83:sex          -0.246     0.127  -1.938   0.053 .  
 user|84:sex           0.125     0.127   0.985   0.325    
 user|85:sex          -0.219     0.127  -1.726   0.085 .  
 user|86:sex          -0.278     0.127  -2.187   0.029 *  
 user|87:sex          -0.067     0.127  -0.531   0.596    
 user|88:sex          -0.236     0.127  -1.853   0.064 .  
 user|89:sex          -0.357     0.127  -2.806   0.005 ** 
 user|90:sex          -0.196     0.127  -1.545   0.123    
 user|91:sex          -0.047     0.127  -0.371   0.711    
 user|92:sex          -0.225     0.127  -1.770   0.077 .  
 user|93:sex          -0.279     0.127  -2.191   0.029 *  
 user|94:sex          -0.108     0.127  -0.846   0.398    
 user|95:sex          -0.023     0.127  -0.180   0.857    
 user|96:sex          -0.280     0.127  -2.199   0.028 *  
 user|97:sex           0.025     0.127   0.196   0.845    
 user|98:sex          -0.062     0.127  -0.485   0.627    
 user|99:sex           0.054     0.127   0.426   0.670    
 user|100:sex         -0.277     0.127  -2.177   0.030 *  
 user|2:action         0.034     0.157   0.218   0.827    
 user|3:action         0.069     0.157   0.437   0.662    
 user|4:action        -0.101     0.157  -0.641   0.522    
 user|5:action        -0.238     0.157  -1.515   0.130    
 user|6:action         0.176     0.157   1.115   0.265    
 user|7:action         0.014     0.157   0.088   0.930    
 user|8:action        -0.109     0.157  -0.695   0.487    
 user|9:action         0.116     0.157   0.734   0.463    
 user|10:action       -0.022     0.157  -0.138   0.890    
 user|11:action       -0.192     0.157  -1.220   0.223    
 user|12:action       -0.047     0.157  -0.296   0.767    
 user|13:action       -0.091     0.157  -0.580   0.562    
 user|14:action       -0.149     0.157  -0.945   0.345    
 user|15:action       -0.095     0.157  -0.604   0.546    
 user|16:action        0.102     0.157   0.645   0.519    
 user|17:action       -0.070     0.157  -0.444   0.657    
 user|18:action       -0.075     0.157  -0.479   0.632    
 user|19:action        0.025     0.157   0.157   0.875    
 user|20:action       -0.221     0.157  -1.403   0.161    
 user|21:action        0.113     0.157   0.718   0.473    
 user|22:action        0.122     0.157   0.776   0.438    
 user|23:action       -0.030     0.157  -0.189   0.850    
 user|24:action        0.104     0.157   0.658   0.511    
 user|25:action        0.180     0.157   1.146   0.252    
 user|26:action        0.359     0.157   2.279   0.023 *  
 user|27:action        0.578     0.157   3.675  < .001 ***
 user|28:action        0.503     0.157   3.198   0.001 ** 
 user|29:action        0.256     0.157   1.629   0.104    
 user|30:action        0.673     0.157   4.274  < .001 ***
 user|31:action        0.453     0.157   2.881   0.004 ** 
 user|32:action        0.367     0.157   2.331   0.020 *  
 user|33:action        0.621     0.157   3.946  < .001 ***
 user|34:action        0.427     0.157   2.715   0.007 ** 
 user|35:action        0.430     0.157   2.732   0.006 ** 
 user|36:action        0.672     0.157   4.267  < .001 ***
 user|37:action        0.473     0.157   3.006   0.003 ** 
 user|38:action        0.337     0.157   2.139   0.033 *  
 user|39:action        0.325     0.157   2.068   0.039 *  
 user|40:action        0.362     0.157   2.303   0.022 *  
 user|41:action        0.579     0.157   3.678  < .001 ***
 user|42:action        0.400     0.157   2.542   0.011 *  
 user|43:action        0.329     0.157   2.092   0.037 *  
 user|44:action        0.307     0.157   1.948   0.052 .  
 user|45:action        0.456     0.157   2.895   0.004 ** 
 user|46:action        0.490     0.157   3.112   0.002 ** 
 user|47:action        0.480     0.157   3.052   0.002 ** 
 user|48:action        0.533     0.157   3.388  < .001 ***
 user|49:action        0.541     0.157   3.437  < .001 ***
 user|50:action        0.394     0.157   2.504   0.012 *  
 user|51:action        0.111     0.157   0.704   0.482    
 user|52:action       -0.173     0.157  -1.100   0.272    
 user|53:action       -0.261     0.157  -1.661   0.097 .  
 user|54:action       -0.027     0.157  -0.171   0.864    
 user|55:action       -0.128     0.157  -0.811   0.418    
 user|56:action       -0.173     0.157  -1.096   0.273    
 user|57:action        0.083     0.157   0.527   0.599    
 user|58:action       -0.005     0.157  -0.035   0.972    
 user|59:action        0.077     0.157   0.489   0.625    
 user|60:action       -0.065     0.157  -0.414   0.679    
 user|61:action        0.206     0.157   1.306   0.192    
 user|62:action       -0.079     0.157  -0.501   0.617    
 user|63:action       -0.089     0.157  -0.565   0.572    
 user|64:action       -0.261     0.157  -1.661   0.097 .  
 user|65:action        0.074     0.157   0.468   0.640    
 user|66:action        0.040     0.157   0.252   0.801    
 user|67:action       -0.174     0.157  -1.107   0.268    
 user|68:action       -0.118     0.157  -0.748   0.455    
 user|69:action       -0.055     0.157  -0.348   0.728    
 user|70:action       -0.046     0.157  -0.291   0.771    
 user|71:action        0.105     0.157   0.664   0.507    
 user|72:action       -0.298     0.157  -1.892   0.059 .  
 user|73:action        0.031     0.157   0.200   0.842    
 user|74:action       -0.057     0.157  -0.363   0.717    
 user|75:action        0.078     0.157   0.495   0.620    
 user|76:action        0.464     0.157   2.949   0.003 ** 
 user|77:action        0.508     0.157   3.227   0.001 ** 
 user|78:action        0.235     0.157   1.496   0.135    
 user|79:action        0.440     0.157   2.795   0.005 ** 
 user|80:action        0.381     0.157   2.424   0.016 *  
 user|81:action        0.488     0.157   3.100   0.002 ** 
 user|82:action        0.219     0.157   1.391   0.165    
 user|83:action        0.396     0.157   2.517   0.012 *  
 user|84:action        0.488     0.157   3.100   0.002 ** 
 user|85:action        0.324     0.157   2.062   0.040 *  
 user|86:action        0.534     0.157   3.394  < .001 ***
 user|87:action        0.536     0.157   3.404  < .001 ***
 user|88:action        0.215     0.157   1.367   0.172    
 user|89:action        0.314     0.157   1.997   0.046 *  
 user|90:action        0.471     0.157   2.990   0.003 ** 
 user|91:action        0.488     0.157   3.101   0.002 ** 
 user|92:action        0.428     0.157   2.721   0.007 ** 
 user|93:action        0.264     0.157   1.681   0.093 .  
 user|94:action        0.550     0.157   3.495  < .001 ***
 user|95:action        0.713     0.157   4.528  < .001 ***
 user|96:action        0.433     0.157   2.749   0.006 ** 
 user|97:action        0.399     0.157   2.538   0.011 *  
 user|98:action        0.567     0.157   3.601  < .001 ***
 user|99:action        0.414     0.157   2.633   0.009 ** 
 user|100:action       0.309     0.157   1.966   0.050 *  
 user|2:story         -0.117     0.157  -0.746   0.456    
 user|3:story         -0.166     0.157  -1.053   0.293    
 user|4:story          0.053     0.157   0.337   0.736    
 user|5:story          0.060     0.157   0.379   0.705    
 user|6:story         -0.175     0.157  -1.111   0.267    
 user|7:story         -0.091     0.157  -0.579   0.563    
 user|8:story          0.113     0.157   0.717   0.474    
 user|9:story         -0.159     0.157  -1.011   0.312    
 user|10:story        -0.070     0.157  -0.446   0.656    
 user|11:story         0.109     0.157   0.695   0.487    
 user|12:story        -0.029     0.157  -0.186   0.853    
 user|13:story        -0.123     0.157  -0.784   0.433    
 user|14:story         0.036     0.157   0.229   0.819    
 user|15:story         0.095     0.157   0.604   0.546    
 user|16:story        -0.137     0.157  -0.868   0.386    
 user|17:story         0.023     0.157   0.149   0.882    
 user|18:story        -0.129     0.157  -0.820   0.412    
 user|19:story        -0.151     0.157  -0.958   0.339    
 user|20:story         0.013     0.157   0.080   0.936    
 user|21:story        -0.049     0.157  -0.310   0.757    
 user|22:story        -0.251     0.157  -1.597   0.111    
 user|23:story        -0.029     0.157  -0.186   0.852    
 user|24:story        -0.108     0.157  -0.685   0.494    
 user|25:story        -0.163     0.157  -1.033   0.302    
 user|26:story         0.023     0.157   0.146   0.884    
 user|27:story        -0.275     0.157  -1.748   0.081 .  
 user|28:story        -0.140     0.157  -0.888   0.375    
 user|29:story        -0.147     0.157  -0.933   0.351    
 user|30:story        -0.198     0.157  -1.257   0.209    
 user|31:story        -0.167     0.157  -1.063   0.288    
 user|32:story        -0.106     0.157  -0.673   0.501    
 user|33:story        -0.332     0.157  -2.107   0.035 *  
 user|34:story        -0.114     0.157  -0.726   0.468    
 user|35:story        -0.144     0.157  -0.914   0.361    
 user|36:story        -0.214     0.157  -1.360   0.174    
 user|37:story        -0.093     0.157  -0.588   0.557    
 user|38:story        -0.080     0.157  -0.510   0.610    
 user|39:story        -0.083     0.157  -0.529   0.597    
 user|40:story        -0.172     0.157  -1.090   0.276    
 user|41:story        -0.247     0.157  -1.569   0.117    
 user|42:story        -0.006     0.157  -0.035   0.972    
 user|43:story         0.010     0.157   0.062   0.951    
 user|44:story        -0.022     0.157  -0.137   0.891    
 user|45:story        -0.108     0.157  -0.684   0.494    
 user|46:story        -0.240     0.157  -1.527   0.127    
 user|47:story        -0.127     0.157  -0.804   0.422    
 user|48:story        -0.134     0.157  -0.851   0.395    
 user|49:story        -0.147     0.157  -0.936   0.350    
 user|50:story        -0.169     0.157  -1.077   0.282    
 user|51:story        -0.267     0.157  -1.696   0.090 .  
 user|52:story        -0.124     0.157  -0.789   0.431    
 user|53:story         0.092     0.157   0.585   0.559    
 user|54:story        -0.171     0.157  -1.088   0.277    
 user|55:story        -0.064     0.157  -0.409   0.683    
 user|56:story        -0.066     0.157  -0.416   0.678    
 user|57:story        -0.291     0.157  -1.847   0.065 .  
 user|58:story        -0.227     0.157  -1.440   0.150    
 user|59:story        -0.195     0.157  -1.238   0.216    
 user|60:story        -0.177     0.157  -1.126   0.261    
 user|61:story        -0.342     0.157  -2.170   0.030 *  
 user|62:story        -0.266     0.157  -1.687   0.092 .  
 user|63:story        -0.139     0.157  -0.881   0.379    
 user|64:story        -0.053     0.157  -0.340   0.734    
 user|65:story        -0.199     0.157  -1.263   0.207    
 user|66:story        -0.262     0.157  -1.663   0.097 .  
 user|67:story         0.098     0.157   0.620   0.536    
 user|68:story        -0.092     0.157  -0.582   0.561    
 user|69:story        -0.052     0.157  -0.333   0.740    
 user|70:story        -0.105     0.157  -0.668   0.504    
 user|71:story        -0.117     0.157  -0.746   0.456    
 user|72:story         0.017     0.157   0.110   0.912    
 user|73:story        -0.271     0.157  -1.721   0.086 .  
 user|74:story        -0.159     0.157  -1.010   0.313    
 user|75:story        -0.189     0.157  -1.199   0.231    
 user|76:story        -0.289     0.157  -1.836   0.067 .  
 user|77:story        -0.216     0.157  -1.375   0.170    
 user|78:story         0.019     0.157   0.122   0.903    
 user|79:story        -0.131     0.157  -0.830   0.407    
 user|80:story        -0.032     0.157  -0.202   0.840    
 user|81:story        -0.108     0.157  -0.687   0.492    
 user|82:story        -0.068     0.157  -0.432   0.666    
 user|83:story        -0.074     0.157  -0.473   0.637    
 user|84:story        -0.257     0.157  -1.630   0.104    
 user|85:story         0.027     0.157   0.169   0.866    
 user|86:story        -0.121     0.157  -0.768   0.443    
 user|87:story        -0.162     0.157  -1.026   0.305    
 user|88:story         0.081     0.157   0.513   0.608    
 user|89:story        -0.046     0.157  -0.292   0.770    
 user|90:story        -0.237     0.157  -1.502   0.133    
 user|91:story        -0.109     0.157  -0.690   0.490    
 user|92:story        -0.108     0.157  -0.688   0.492    
 user|93:story        -0.021     0.157  -0.132   0.895    
 user|94:story        -0.178     0.157  -1.128   0.260    
 user|95:story        -0.416     0.157  -2.639   0.008 ** 
 user|96:story        -0.242     0.157  -1.538   0.124    
 user|97:story        -0.037     0.157  -0.236   0.814    
 user|98:story        -0.324     0.157  -2.058   0.040 *  
 user|99:story        -0.211     0.157  -1.341   0.180    
 user|100:story       -0.103     0.157  -0.653   0.514    

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

R-squared: 0.876,  Adjusted R-squared: 0.799 
F-statistic: 11.352 df(499,800), p.value < .001
Nr obs: 1,300 
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
Linear regression (OLS)
Data                 : ulratings_wrk 
Filter               : training == 1 
Response variable    : rating 
Explanatory variables: user, talk, sex, action, story 
Prediction dataset   : ulratings_wrk 
Rows shown           : 10 of 2,500 

 user talk sex action story Prediction   2.5% 97.5%   +/-
    1    1   7      9     9      0.871 -0.543 2.285 1.414
    1    9   5      6     6      4.706  3.306 6.106 1.400
    1    1   4     10     7      1.184 -0.240 2.608 1.424
    1   10   6      1     2      5.367  3.860 6.874 1.507
    1    3   7      5     5      1.997  0.601 3.394 1.396
    1    7   9      8     7      3.073  1.689 4.457 1.384
    1    9   8      8     4      3.970  2.362 5.578 1.608
    1    2  10     10     9      0.706 -0.759 2.170 1.464
    1    6   1      7     4      3.943  2.420 5.465 1.523
    1    5   7      9    10      2.545  1.115 3.976 1.431
store(pred, data = "ulratings_wrk", name = "customized")

Lets take a look at the Data > View tab after setting Filter to training == 0. Make sure all variables are selected so you can see the difference between aggregate and customized for the different movies.

updateSelectInput(session, "dataset", selected = "ulratings_wrk")
updateCheckboxInput(session, "show_filter", value = TRUE)
updateTextInput(session, "data_filter", value = "training == 1")
updateTabsetPanel(session, "tabs_data", selected = "View")
updateTabsetPanel(session, "nav_radiant", selected = "Data")

Finally, generate predictions for each user separately (note the filter), using only the story information about a movie.

result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("user", "story"), 
  int = "user:story", 
  data_filter = "training == 1"
)
summary(result)
Linear regression (OLS)
Data     : ulratings_wrk 
Filter   : training == 1 
Response variable    : rating 
Explanatory variables: user, story 
Null hyp.: the effect of x on rating is zero
Alt. hyp.: the effect of x on rating is not zero

                coefficient std.error t.value p.value    
 (Intercept)          3.964     0.760   5.215  < .001 ***
 user|2               0.114     1.075   0.106   0.916    
 user|3               0.897     1.075   0.834   0.404    
 user|4              -0.335     1.075  -0.311   0.756    
 user|5               0.072     1.075   0.067   0.947    
 user|6               0.586     1.075   0.545   0.586    
 user|7               0.490     1.075   0.456   0.649    
 user|8              -0.866     1.075  -0.806   0.420    
 user|9               1.122     1.075   1.044   0.297    
 user|10             -0.029     1.075  -0.027   0.979    
 user|11             -0.092     1.075  -0.086   0.932    
 user|12              0.369     1.075   0.344   0.731    
 user|13              0.700     1.075   0.651   0.515    
 user|14             -0.322     1.075  -0.299   0.765    
 user|15             -0.326     1.075  -0.303   0.762    
 user|16              0.461     1.075   0.429   0.668    
 user|17              0.059     1.075   0.055   0.956    
 user|18             -0.024     1.075  -0.023   0.982    
 user|19              0.846     1.075   0.787   0.431    
 user|20             -0.753     1.075  -0.700   0.484    
 user|21              0.230     1.075   0.214   0.831    
 user|22              1.177     1.075   1.095   0.274    
 user|23             -0.473     1.075  -0.440   0.660    
 user|24              0.825     1.075   0.767   0.443    
 user|25              0.624     1.075   0.580   0.562    
 user|26             -1.355     1.075  -1.260   0.208    
 user|27             -0.714     1.075  -0.664   0.507    
 user|28             -0.576     1.075  -0.536   0.592    
 user|29             -1.187     1.075  -1.104   0.270    
 user|30             -1.020     1.075  -0.949   0.343    
 user|31             -1.103     1.075  -1.027   0.305    
 user|32             -1.434     1.075  -1.334   0.183    
 user|33             -0.651     1.075  -0.605   0.545    
 user|34             -1.062     1.075  -0.988   0.323    
 user|35             -0.990     1.075  -0.921   0.357    
 user|36             -0.773     1.075  -0.719   0.472    
 user|37             -1.718     1.075  -1.599   0.110    
 user|38             -1.430     1.075  -1.330   0.184    
 user|39             -0.638     1.075  -0.593   0.553    
 user|40             -1.655     1.075  -1.540   0.124    
 user|41             -0.806     1.075  -0.750   0.454    
 user|42             -1.639     1.075  -1.525   0.128    
 user|43             -1.668     1.075  -1.552   0.121    
 user|44             -1.379     1.075  -1.283   0.200    
 user|45             -1.397     1.075  -1.299   0.194    
 user|46             -0.705     1.075  -0.656   0.512    
 user|47             -1.296     1.075  -1.206   0.228    
 user|48             -1.392     1.075  -1.295   0.196    
 user|49             -1.259     1.075  -1.171   0.242    
 user|50             -0.714     1.075  -0.664   0.507    
 user|51             -1.052     1.075  -0.978   0.328    
 user|52             -0.172     1.075  -0.160   0.873    
 user|53             -1.386     1.075  -1.290   0.197    
 user|54             -0.542     1.075  -0.504   0.614    
 user|55             -0.743     1.075  -0.691   0.490    
 user|56             -1.664     1.075  -1.548   0.122    
 user|57             -1.307     1.075  -1.216   0.224    
 user|58             -0.277     1.075  -0.258   0.796    
 user|59             -0.625     1.075  -0.581   0.561    
 user|60             -1.168     1.075  -1.087   0.277    
 user|61             -1.520     1.075  -1.414   0.158    
 user|62             -0.348     1.075  -0.323   0.746    
 user|63             -0.759     1.075  -0.706   0.481    
 user|64             -1.216     1.075  -1.131   0.258    
 user|65             -1.981     1.075  -1.843   0.066 .  
 user|66             -1.336     1.075  -1.243   0.214    
 user|67             -2.320     1.075  -2.159   0.031 *  
 user|68             -1.270     1.075  -1.182   0.238    
 user|69             -2.257     1.075  -2.100   0.036 *  
 user|70             -1.198     1.075  -1.115   0.265    
 user|71             -2.274     1.075  -2.116   0.035 *  
 user|72             -0.989     1.075  -0.920   0.358    
 user|73             -0.918     1.075  -0.854   0.393    
 user|74             -1.756     1.075  -1.633   0.103    
 user|75             -1.358     1.075  -1.263   0.207    
 user|76             -0.575     1.075  -0.535   0.593    
 user|77             -0.211     1.075  -0.196   0.844    
 user|78             -1.240     1.075  -1.153   0.249    
 user|79             -0.098     1.075  -0.091   0.928    
 user|80             -1.131     1.075  -1.052   0.293    
 user|81             -0.734     1.075  -0.683   0.495    
 user|82             -0.599     1.075  -0.557   0.577    
 user|83             -0.378     1.075  -0.352   0.725    
 user|84              0.119     1.075   0.111   0.912    
 user|85             -0.684     1.075  -0.636   0.525    
 user|86             -0.809     1.075  -0.753   0.452    
 user|87             -0.500     1.075  -0.465   0.642    
 user|88              0.091     1.075   0.084   0.933    
 user|89             -0.783     1.075  -0.728   0.466    
 user|90             -0.159     1.075  -0.148   0.882    
 user|91             -0.320     1.075  -0.298   0.766    
 user|92             -0.378     1.075  -0.352   0.725    
 user|93             -1.030     1.075  -0.958   0.338    
 user|94             -0.638     1.075  -0.593   0.553    
 user|95             -0.612     1.075  -0.569   0.569    
 user|96             -0.904     1.075  -0.841   0.401    
 user|97             -0.191     1.075  -0.178   0.859    
 user|98             -0.273     1.075  -0.254   0.800    
 user|99              0.517     1.075   0.481   0.630    
 user|100            -0.494     1.075  -0.460   0.646    
 story               -0.208     0.125  -1.667   0.096 .  
 user|2:story        -0.064     0.176  -0.363   0.717    
 user|3:story        -0.138     0.176  -0.783   0.434    
 user|4:story         0.005     0.176   0.029   0.977    
 user|5:story        -0.085     0.176  -0.481   0.630    
 user|6:story        -0.052     0.176  -0.294   0.769    
 user|7:story        -0.048     0.176  -0.273   0.785    
 user|8:story         0.032     0.176   0.184   0.854    
 user|9:story        -0.094     0.176  -0.534   0.593    
 user|10:story       -0.066     0.176  -0.375   0.708    
 user|11:story       -0.011     0.176  -0.065   0.948    
 user|12:story       -0.126     0.176  -0.714   0.476    
 user|13:story       -0.116     0.176  -0.657   0.512    
 user|14:story       -0.040     0.176  -0.228   0.819    
 user|15:story       -0.025     0.176  -0.143   0.887    
 user|16:story       -0.114     0.176  -0.648   0.517    
 user|17:story       -0.040     0.176  -0.224   0.823    
 user|18:story       -0.081     0.176  -0.461   0.645    
 user|19:story       -0.129     0.176  -0.730   0.466    
 user|20:story       -0.032     0.176  -0.179   0.858    
 user|21:story        0.029     0.176   0.163   0.870    
 user|22:story       -0.119     0.176  -0.673   0.501    
 user|23:story       -0.012     0.176  -0.069   0.945    
 user|24:story       -0.053     0.176  -0.302   0.763    
 user|25:story       -0.016     0.176  -0.090   0.929    
 user|26:story        0.409     0.176   2.320   0.021 *  
 user|27:story        0.333     0.176   1.888   0.059 .  
 user|28:story        0.350     0.176   1.986   0.047 *  
 user|29:story        0.320     0.176   1.819   0.069 .  
 user|30:story        0.404     0.176   2.292   0.022 *  
 user|31:story        0.362     0.176   2.055   0.040 *  
 user|32:story        0.352     0.176   1.998   0.046 *  
 user|33:story        0.278     0.176   1.578   0.115    
 user|34:story        0.383     0.176   2.173   0.030 *  
 user|35:story        0.298     0.176   1.692   0.091 .  
 user|36:story        0.372     0.176   2.112   0.035 *  
 user|37:story        0.348     0.176   1.974   0.049 *  
 user|38:story        0.337     0.176   1.912   0.056 .  
 user|39:story        0.233     0.176   1.321   0.187    
 user|40:story        0.293     0.176   1.664   0.096 .  
 user|41:story        0.321     0.176   1.823   0.069 .  
 user|42:story        0.404     0.176   2.296   0.022 *  
 user|43:story        0.338     0.176   1.921   0.055 .  
 user|44:story        0.328     0.176   1.859   0.063 .  
 user|45:story        0.388     0.176   2.202   0.028 *  
 user|46:story        0.302     0.176   1.717   0.086 .  
 user|47:story        0.369     0.176   2.096   0.036 *  
 user|48:story        0.373     0.176   2.116   0.035 *  
 user|49:story        0.405     0.176   2.300   0.022 *  
 user|50:story        0.333     0.176   1.888   0.059 .  
 user|51:story        0.181     0.176   1.028   0.304    
 user|52:story        0.103     0.176   0.587   0.557    
 user|53:story        0.186     0.176   1.056   0.291    
 user|54:story        0.229     0.176   1.301   0.194    
 user|55:story        0.267     0.176   1.513   0.131    
 user|56:story        0.323     0.176   1.835   0.067 .  
 user|57:story        0.243     0.176   1.378   0.168    
 user|58:story        0.137     0.176   0.779   0.436    
 user|59:story        0.187     0.176   1.064   0.287    
 user|60:story        0.088     0.176   0.502   0.616    
 user|61:story        0.154     0.176   0.873   0.383    
 user|62:story        0.050     0.176   0.285   0.775    
 user|63:story        0.155     0.176   0.881   0.379    
 user|64:story        0.254     0.176   1.444   0.149    
 user|65:story        0.268     0.176   1.521   0.129    
 user|66:story        0.177     0.176   1.003   0.316    
 user|67:story        0.288     0.176   1.635   0.102    
 user|68:story        0.279     0.176   1.582   0.114    
 user|69:story        0.233     0.176   1.325   0.185    
 user|70:story        0.194     0.176   1.101   0.271    
 user|71:story        0.294     0.176   1.668   0.096 .  
 user|72:story        0.126     0.176   0.718   0.473    
 user|73:story        0.213     0.176   1.211   0.226    
 user|74:story        0.312     0.176   1.770   0.077 .  
 user|75:story        0.252     0.176   1.431   0.153    
 user|76:story        0.178     0.176   1.011   0.312    
 user|77:story        0.239     0.176   1.358   0.175    
 user|78:story        0.173     0.176   0.983   0.326    
 user|79:story        0.175     0.176   0.995   0.320    
 user|80:story        0.124     0.176   0.705   0.481    
 user|81:story        0.236     0.176   1.342   0.180    
 user|82:story        0.097     0.176   0.551   0.582    
 user|83:story        0.156     0.176   0.885   0.376    
 user|84:story        0.249     0.176   1.415   0.157    
 user|85:story        0.227     0.176   1.289   0.198    
 user|86:story        0.165     0.176   0.934   0.351    
 user|87:story        0.250     0.176   1.419   0.156    
 user|88:story        0.183     0.176   1.040   0.299    
 user|89:story        0.074     0.176   0.420   0.675    
 user|90:story        0.058     0.176   0.330   0.741    
 user|91:story        0.288     0.176   1.635   0.102    
 user|92:story        0.156     0.176   0.885   0.376    
 user|93:story        0.106     0.176   0.599   0.549    
 user|94:story        0.233     0.176   1.321   0.187    
 user|95:story        0.142     0.176   0.807   0.420    
 user|96:story       -0.004     0.176  -0.020   0.984    
 user|97:story        0.335     0.176   1.904   0.057 .  
 user|98:story        0.122     0.176   0.693   0.488    
 user|99:story        0.190     0.176   1.077   0.282    
 user|100:story       0.063     0.176   0.359   0.720    

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

R-squared: 0.289,  Adjusted R-squared: 0.161 
F-statistic: 2.252 df(199,1100), p.value < .001
Nr obs: 1,300 
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
Linear regression (OLS)
Data                 : ulratings_wrk 
Filter               : training == 1 
Response variable    : rating 
Explanatory variables: user, story 
Prediction dataset   : ulratings_wrk 
Rows shown           : 10 of 2,500 

 user story Prediction   2.5% 97.5%   +/-
    1     9      2.096 -0.674 4.865 2.770
    1     6      2.718  0.089 5.347 2.629
    1     7      2.511 -0.144 5.165 2.654
    1     2      3.549  0.797 6.301 2.752
    1     5      2.926  0.300 5.552 2.626
    1     7      2.511 -0.144 5.165 2.654
    1     4      3.134  0.487 5.780 2.646
    1     9      2.096 -0.674 4.865 2.770
    1     4      3.134  0.487 5.780 2.646
    1    10      1.888 -0.969 4.745 2.857
store(pred, data = "ulratings_wrk", name = "customized_story")

Plotting the performance of the Content based RS

result <- evalreg(
  dataset = "ulratings_wrk", 
  pred = c("aggregate", "user1", "customized", "customized_story"), 
  rvar = "rating", 
  train = "Both", 
  data_filter = "training == 1"
)
summary(result)
Evaluate predictions for regression models
Data        : ulratings_wrk 
Filter      : training == 1 
Results for : Both 
Predictors  : aggregate, user1, customized, customized_story 
Response    : rating 

       Type        Predictor   Rsq  RMSE   MAE
   Training        aggregate 0.048 1.372 1.181
   Training            user1 0.010 2.161 1.705
   Training       customized 0.876 0.495 0.400
   Training customized_story 0.289 1.186 0.990
 Validation        aggregate 0.045 1.386 1.193
 Validation            user1 0.011 1.853 1.476
 Validation       customized 0.682 0.818 0.653
 Validation customized_story 0.043 1.472 1.203
plot(result)

visualize(
  dataset = "ulratings_wrk", 
  xvar = "aggregate", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from aggregate attribute regression",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")

visualize(
  dataset = "ulratings_wrk", 
  xvar = "user1", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from user1 attribute regression",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")

visualize(
  dataset = "ulratings_wrk", 
  xvar = "customized", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from user-level attribute regressions",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")

What if we only had data on the ‘story’ attribute?

visualize(
  dataset = "ulratings_wrk", 
  xvar = "customized_story", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from user-level single-attribute regressions (story)",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")

---
title: ""
output:
  html_notebook:
    highlight: textmate
    theme: spacelab
    toc: yes
    code_folding: hide
---

```{r r_setup, include = FALSE}
## initial settings
knitr::opts_chunk$set(
  comment = NA,
  echo = TRUE,
  error = TRUE,
  cache = FALSE,
  message = FALSE,
  dpi = 96,
  warning = FALSE
)

## width to use when printing tables etc.
options(
  width = 250,
  scipen = 100,
  max.print = 5000,
  stringsAsFactors = FALSE
)

## make all required libraries available by loading radiant package if needed
if (!exists("r_environment")) library(radiant)

## load data (add path)
# r_data <- readr::read_rds("r_data.rds")
```

<style>
.table {
  width: auto;
}
ul, ol {
  padding-left: 18px;
}
pre, code, pre code {
  overflow: auto;
  white-space: pre;
  word-wrap: normal;
  background-color: #ffffff;
}
</style>

# Recommendation systems

Loading the data directly into radiant

```{r}
if (file.exists("data/cf_demo.rds")) {
  r_data[["cf_demo_wrk"]] <- readr::read_rds("data/cf_demo.rds")
  register("cf_demo_wrk")
  
  r_data[["ratings0_wrk"]] <- readr::read_rds("data/ratings0.rds")
  register("ratings0_wrk")
  
  r_data[["ratings50_wrk"]] <- readr::read_rds("data/ratings50.rds")
  register("ratings50_wrk")
  
  r_data[["ratings80_wrk"]] <- readr::read_rds("data/ratings80.rds")
  register("ratings80_wrk")
  
  r_data[["ulratings_wrk"]] <- readr::read_rds("data/ulratings.rds")
  register("ulratings_wrk")
} else {
  stop("Are you using the Rstudio project folder for 'crs'?\\nIt should say 'Project: crs' at the top-right of your screen",  call. = FALSE)
}
```

## Demo: Colaborative Filtering

We will start with the `cf_demo_wrk` data. See the dataset description in _Data > Manage_. The results are equivalent to what you will see in the `cf_demo.xlsx` file. Note the data filter used to estimate the model on a training dataset (i.e., U1-U10) and predict for user U11

```{r}
result <- crs(
  dataset = "cf_demo_wrk", 
  id = "users", 
  prod = "movies", 
  pred = c("M6", "M7", "M8", "M9", "M10"), 
  rate = "ratings", 
  data_filter = "training == 1"
)
summary(result)
```

## Frequency plot of ratings in the validation data

For user in the hold-out sample we have ratings on movies 14-25

```{r fig.width = 4, fig.height = 5, dpi = 120}
result <- pivotr(
  dataset = "ratings0_wrk", 
  cvars = "rating", 
  data_filter = "training == 0 & product %in% paste0('mov', 14:25)", 
  nr = 5
)
plot(result, custom = TRUE) +
  labs(title = "Ratings for users 61-100 on movies 14-25")
```

## Generating recommendations with CF

```{r fig.width = 7, fig.height = 5.54, dpi = 200}
result <- crs(
  dataset = "ratings0_wrk", 
  id = "user", 
  prod = "product", 
  pred = "mov14:mov25", 
  rate = "rating", 
  data_filter = "training == 1"
)
summary(result)
plot(result)
```

```{r fig.width = 7, fig.height = 5.54, dpi = 200}
result <- crs(
  dataset = "ratings50_wrk", 
  id = "user", 
  prod = "product", 
  pred = "mov14:mov25", 
  rate = "rating", 
  data_filter = "training == 1"
)
plot(result)
```

```{r fig.width = 7, fig.height = 5.54, dpi = 200}
result <- crs(
  dataset = "ratings80_wrk", 
  id = "user", 
  prod = "product", 
  pred = "mov14:mov25", 
  rate = "rating", 
  data_filter = "training == 1"
)
plot(result)
```

## Predictions using regression across all users

```{r}
result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("talk", "sex", "action", "story"), 
  data_filter = "training == 1"
)
summary(result)
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
store(pred, data = "ulratings_wrk", name = "aggregate")
```

## User-level predictions using regression

Predictions based only on ratings from `user 1` on movies 1-13 (note the filter).

```{r}
result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("talk", "sex", "action", "story"), 
  data_filter = "user == 1 & training == 1"
)
summary(result)
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
store(pred, data = "ulratings_wrk", name = "user1")
```

Generate predictions for each user separately (note the filter). We could use a `loop` to do this but we can actually get predictions for all users using interaction terms. Hint: You might need something like this for Pentathlon III

```{r}
result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("user", "talk", "sex", "action", "story"), 
  int = c("user:talk", "user:sex", "user:action", "user:story"), 
  data_filter = "training == 1"
)
summary(result)
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
store(pred, data = "ulratings_wrk", name = "customized")
```

Lets take a look at the _Data > View_ tab after setting `Filter` to `training == 0`. Make sure all variables are selected so you can see the difference between `aggregate` and `customized` for the different movies.

```{r}
updateSelectInput(session, "dataset", selected = "ulratings_wrk")
updateCheckboxInput(session, "show_filter", value = TRUE)
updateTextInput(session, "data_filter", value = "training == 1")
updateTabsetPanel(session, "tabs_data", selected = "View")
updateTabsetPanel(session, "nav_radiant", selected = "Data")
```

Finally, generate predictions for each user separately (note the filter), using only the `story` information about a movie. 

```{r}
result <- regress(
  dataset = "ulratings_wrk", 
  rvar = "rating", 
  evar = c("user", "story"), 
  int = "user:story", 
  data_filter = "training == 1"
)
summary(result)
pred <- predict(result, pred_data = "ulratings_wrk")
print(pred, n = 10)
store(pred, data = "ulratings_wrk", name = "customized_story")
```

## Plotting the performance of the Content based RS

```{r fig.width = 7, fig.height = 6, dpi = 200}
result <- evalreg(
  dataset = "ulratings_wrk", 
  pred = c("aggregate", "user1", "customized", "customized_story"), 
  rvar = "rating", 
  train = "Both", 
  data_filter = "training == 1"
)
summary(result)
plot(result)
```

```{r fig.width = 10, fig.height = 6, dpi = 200}
visualize(
  dataset = "ulratings_wrk", 
  xvar = "aggregate", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from aggregate attribute regression",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")
```

```{r fig.width = 10, fig.height = 6, dpi = 200}
visualize(
  dataset = "ulratings_wrk", 
  xvar = "user1", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from user1 attribute regression",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")
```

```{r fig.width = 10, fig.height = 6, dpi = 200}
visualize(
  dataset = "ulratings_wrk", 
  xvar = "customized", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from user-level attribute regressions",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")
```

## What if we only had data on the 'story' attribute?

```{r fig.width = 10, fig.height = 6, dpi = 200}
visualize(
  dataset = "ulratings_wrk", 
  xvar = "customized_story", 
  yvar = "rating", 
  type = "scatter", 
  facet_col = "movie", 
  check = "line", 
  data_filter = "training == 0 & movie %in% paste0('mov', 14:25)", 
  custom = TRUE
) +
  geom_segment(aes(x = 1, y = 1, xend = 5, yend = 5), color = "blue", size = .05) +
  coord_cartesian(xlim = c(1,5), ylim = c(1,5)) +
  labs(
    title = "Predictions from user-level single-attribute regressions (story)",
    x = "Predicted ratings",
    y = "Actual ratings"
  ) +
  theme(legend.position = "none")
```

